基於Python的Grib數據可視化

        利用Python語言實現Grib數據可視化主要依靠三個庫——pygrib、numpy和matplotlib。pygrib是歐洲中期天氣預報中心(ECMWF)的GRIG API C庫的Python接口，經過這個庫能夠將Grib數據讀取出來；numpy是Python的一種開源的數值計算擴展，這種工具可用來存儲和處理大型矩陣；matplotlib是python著名的繪圖庫，它提供了一整套和matlab類似的命令API，十分適合交互式地進行製圖；在數據可視化過程當中，咱們常須要將數據在地圖上畫出來，因此還須要matplotlib的一個子包basemap，負責地圖繪製。

1、庫的安裝

    （一）matplotlib安裝

• matplotlib依賴

1. nose

2. numpy

3. pyparsing

4. python-dateutil

5. cycler

6. pkg-config

7. freetype

8. libpng

• 安裝過程

        這裏我都是經過源碼包安裝的，你們也能夠再終端裏經過pip install 命令來安裝 

        一、安裝nose

            解壓縮後，進入命令提示符 運行

1 python3 setup.py install

        二、安裝numpy

            解壓縮後，進入命令提示符 運行

1 python3 setup.py install

       三、安裝pyparsing

            解壓縮後，進入命令提示符 運行

1 python3 setup.py install

        四、安裝python-dateutil

            解壓縮後，進入命令提示符 運行

1 python3 setup.py install

        五、安裝cycler

            解壓縮後，進入命令提示符 運行

1 python3 setup.py install

       六、安裝pkg-config

1 ./configure --with-intermal-glib
2 make && date
3 sudo make install && date

        七、安裝freetype

1  ./configure
2  make && date
3  sudo make install && date

        八、安裝libpng

1  ./configure
2  make && date
3  sudo make install && date

        九、安裝matplotlib-1.5.0

            解壓縮後，進入命令提示符 運行

1 python3 setup.py install

    （二）basemap安裝

• basemap依賴

1. geos

2. pyproj

• 安裝過程

        一、安裝GEOS

 

1  ./configure
2  make && date
3  sudo make install && date

 

        二、安裝pyproj

1 python3 setup.py install

        三、安裝basemap

1 python3 setup.py install

    （三）pygrib安裝

• pygrib依賴

1. Jasper 

2. GRIB API 

3. numpy

4. pyproj

• 安裝過程

            因爲以前已經安裝了numpy和pyproj，這裏只需安裝Jasper和GRIB API便可安裝pygrib

        一、安裝Jasper

1 ./configure
2 make && date
3 sudo make install && date

        二、安裝GRIB API

1 ./configure --with-jasper='/usr/local/'
2 make && date
3 sudo make install && date

        三、安裝pygrib

            安裝pygrib以前首先要根據本身的實際狀況修改文件目錄下的setup.cfg文件，最主要的就是修改grib_api_dir和jasper_dir，這兩個是剛剛安裝的Jasper和GRIB API的路徑，若是這兩個地址不正確安裝會報錯

 1 # Rename this file to setup.cfg to set pygrib's
 2 # build options.
 3 # Follow instructions below for editing.
 4 [directories]
 5 # uncomment and set to grib_api install location.
 6 # Include files should be located in grib_api_dir/include and
 7 # the library should be located in grib_api_dir/lib.
 8 # If the libraries and include files are installed in separate locations,
 9 # use grib_api_libdir and grib_api_incdir to specify the locations
10 # separately.  
11 grib_api_dir = /usr/local
12 # if grib_api was built with jasper support for JPEG200,
13 # uncomment and set to jasper lib install location.
14 # If the libraries and include files are installed in separate locations,
15 # use jasper_libdir and jasper_incdir.
16 jasper_dir = /usr/local
17 # if grib_api was built with openjpeg support for JPEG200,
18 # uncomment and set to openjpeg lib install location.
19 # If the libraries and include files are installed in separate locations,
20 # use openjpeg_libdir and openjpeg_incdir.
21 #openjpeg_dir = /opt/local
22 # if grib_api was built with png support,
23 # uncomment and set to png lib install location.
24 # If the libraries and include files are installed in separate locations,
25 # use png_libdir and png_incdir.
26 png_dir = /usr
27 # if grib_api was built with png support,
28 # uncomment and set to zlib install location.
29 zlib_dir = /usr
30 # install man pages for command line utilities here
31 #man_dir = /usr/local/man

View Code

           修改好就能夠正常安裝了

1 python3 setup.py install

2、grib數據讀取

        雖然我作的東西和睦象沾邊，可是我自己並非氣象專業出身，全部這些東西都是我慢慢研究琢磨出來的，因此有些方面可能講的比較外行，有不對的地方歡迎你們留言指正。

    （一）導入pygrib模塊

1 >>> import pygrib

    （二）打開Grib文件

1 >>> grbs = pygrib.open('/Users/Kallan/Documents/data/echhae50.082')

    （三）提取文件信息

1 >>> grbs.seek(0)
2 >>> for grb in grbs:
3 grb
4 1:Geopotential Height:gpm (instant):regular_ll:isobaricInhPa:level 500:fcst time 24 :from 201507081200

       信息解讀    

        1 :數據列表的行號，有的文件可能包括多個數據

        Geopotential Height:數據的名稱

        gpm (instant):數據的單位

        regular_ll:常規數據，其實這個字段我也不清楚

        isobaricInhPa:這個字段表示的是數據屬性，此處表示是以hPa爲單位的等壓面

        level 500:這個字段表示的是高度層

        fcst time 24 :預報時效

        from 201507081200 :起報時間

    綜合上面的信息能夠得出，這個文件是從2015年7月8日12時開始的24小時後500hPa等壓面高度場數據

    （四）導出文件數據

 1 >>> grb = grbs.select(name='Geopotential Height')[0]
 2 >>> data = grb.values
 3 >>> print(data.shape,data.min(),data.max())
 4 (37, 37) 5368.6796875 5941.0390625
 5 >>> lat,lon=grb.latlons()
 6 >>> print(lat,'\n',lon)
 7 [[  0.    0.    0.  ...,   0.    0.    0. ]
 8 [  2.5   2.5   2.5 ...,   2.5   2.5   2.5]
 9 [  5.    5.    5.  ...,   5.    5.    5. ]
10 ...,
11 [ 85.   85.   85.  ...,  85.   85.   85. ]
12 [ 87.5  87.5  87.5 ...,  87.5  87.5  87.5]
13 [ 90.   90.   90.  ...,  90.   90.   90. ]]
14 [[-90.  -87.5 -85.  ...,  -5.   -2.5   0. ]
15 [-90.  -87.5 -85.  ...,  -5.   -2.5   0. ]
16 [-90.  -87.5 -85.  ...,  -5.   -2.5   0. ]
17 ...,
18 [-90.  -87.5 -85.  ...,  -5.   -2.5   0. ]
19 [-90.  -87.5 -85.  ...,  -5.   -2.5   0. ]
20 [-90.  -87.5 -85.  ...,  -5.   -2.5   0. ]]

 

3、grib數據可視化

    （一）導入須要的模塊

1 >>> import matplotlib.pyplot as plt
2 >>> from mpl_toolkits.basemap import Basemap
3 >>> import numpy as np

    （二）建立一個figure

1 >>> plt.figure()
2 <matplotlib.figure.Figure object at 0x107e65198>

    （三）建立一個basemap實例

 1 >>> m=Basemap(projection='mill',lat_ts=10,llcrnrlon=lon.min(), \
 2  urcrnrlon=lon.max(),llcrnrlat=lat.min(),urcrnrlat=lat.max(), \
 3  resolution='c')
 4 >>> m.drawcoastlines(linewidth=0.25)
 5 <matplotlib.collections.LineCollection object at 0x1091c1f28>
 6 >>> m.drawcountries(linewidth=0.25)
 7 <matplotlib.collections.LineCollection object at 0x10621d0f0>
 8 >>> m.fillcontinents(color='coral',lake_color='aqua')
 9 >>> m.drawmapboundary(fill_color='aqua')
10 <matplotlib.patches.Rectangle object at 0x10918b3c8>
11 >>> m.drawmeridians(np.arange(0,360,30))
12 >>> m.drawparallels(np.arange(-90,90,30))

    （四）將lat，lon的數據格式轉換成投影須要的格式存入x，y

1 >>> x, y = m(lon,lat)

    （五）繪製等值線

1 >>> cs = m.contour(x,y,data,15,linewidths=1.5)

    （六）命名並顯示圖像

1 >>> plt.title('Geopotential Height Contour from Grib')
2 <matplotlib.text.Text object at 0x10918bda0>
3 >>> plt.show()

    （七）圖像展現

 

 

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